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Query: UMLS:C0020538 (hypertension)
 170,190 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Many complex diseases are related to genetics, and it is of great interest to evaluate the association between single-nucleotide polymorphisms (SNPs) and disease outcome. The association of genetics with outcome can be modified by covariates such as age, sex, smoking status, and membership to the same pedigree. In this paper, we propose a block entropy method to separate two classes of SNPs, for which the association with hypertension is either sensitive or insensitive to the covariates. We also propose a consistency entropy method to further reduce the number of SNPs that might be associated with the outcome. Based on the data provided by the organizers of Genetic Analysis Workshop 18, we calculated the block entropies for six different blocking strategies. Using block entropy and consistency entropy, we identified 230 SNPs on chromosome 9 that are most likely to be associated with the outcome and whose associations with hypertension are sensitive to the covariates.
BMC Proc 2014
PMID:Entropy-based method for assessing the influence of genetic markers and covariates on hypertension: application to Genetic Analysis Workshop 18 data. 2551 19

As the availability of cost-effective high-throughput sequencing technology increases, genetic research is beginning to focus on identifying the contributions of rare variants (RVs) to complex traits. Using RVs to detect associated genes requires statistical approaches that mitigate the lack of power with the analysis of single RVs. Here we report the development and application of an approach that aggregates and evaluates the transmissions of RVs in parent-child trios. An initial score that incorporates the distortion in transmission of the observed RVs from the parents to their offspring is calculated for each variant. The scores are analyzed using a support vector machine that handles these data by mapping the transmission distortion of the multiple RVs into a one-dimensional score in a nonlinear fashion when parent-child trios with affected and nonaffected children are contrasted. We refer to this approach as Trio-SVM. A total of 275 trios were available in the Genetic Analysis Workshop 18 data for analysis. Because of their nonindependence and the extended linkage disequilibrium (LD) within pedigrees, Trio-SVM was vulnerable to type I errors in detecting association. Using the GAW18 data with simulated trait values, Trio-SVM has an appropriate type I error, but it lacks power with a sample of 267 trios. Larger samples of 500 to 1000 trios, derived from combining the simulated data, provided sufficient power. Two chromosome 3 candidate genes were tested in the real GAW18 data with Trio-SVM, and they showed marginal associations with hypertension.
BMC Proc 2014
PMID:Identifying rare-variant associations in parent-child trios using a Gaussian support vector machine. 2551 20

Graphical models are increasingly used in genetic analyses to take into account the complex relationships between genetic and nongenetic factors influencing the phenotypes. We propose a model for determining the network structure of quantitative traits while accounting for the correlated nature of the family-based samples using the kinship coefficient. The Gaussian graphical model of age, systolic blood pressure, diastolic blood pressure, hypertension, blood pressure medication use, and smoking status was derived for three time points using real data. We also explored binary sparse graphical models of single-nucleotide polymorphisms (SNPs), covariates, and quantitative traits for exploratory analysis of the data. We validated the applicability of this method by producing a network graph using 20 causal variants, 21 noncausal variants, and 6 binary and quantitative phenotypes using the simulated data. To improve the model's ability to identify associations between the causal variants and the phenotypes, we intend to conduct follow-up studies investigating how to use the relationships between SNPs and between SNPs and phenotypes when analyzing genome wide association data with multiple phenotypes.
BMC Proc 2014
PMID:Gaussian graphical models for phenotypes using pedigree data and exploratory analysis using networks with genetic and nongenetic factors based on Genetic Analysis Workshop 18 data. 2551 21

Elevated blood pressure is an important global health problem, and in-utero under-nutrition may be an important factor in the pathogenesis of hypertension. In the present study, we tested the hypothesis that antenatal maternal low protein diet (MLPD) leads to sexually dimorphic developmental programming of the components of the pulmonary renin-angiotensin system. This may be important in the antenatal MLPD-associated development of hypertension. In pregnant mice, we administered normal (control) and isocaloric 50% protein restricted diet, commencing one week before mating and continuing until delivery of the pups. From the 18th to 24th week postnatal, we measured blood pressure in the offspring by use of a non-invasive tail-cuff method. In the same mice, we examined the mRNA and protein expression of the key components of the pulmonary renin-angiotensin system. Also, we examined microRNA complementary to angiotensin converting enzymes (ACE) 2 in the offspring lungs. Our results demonstrate that as a consequence of antenatal MLPD: 1) pup birthweight was significantly reduced in both sexes. 2) female offspring developed hypertension, but males did not. 3) In female offspring, ACE-2 protein expression was significantly reduced without any change in the mRNA levels. 4) miRNA 429, which has a binding site on ACE-2 - 3' UTR was significantly upregulated in the female antenatal MLPD offspring. 5) In males, ACE-2 mRNA and protein expression were unaltered. We conclude that in the mouse, antenatal MLPD-induced reduction of ACE-2 in the female offspring lung may be an important mechanisms in sexually dimorphic programming of hypertension.
BMC Physiol 2015 May 14
PMID:Antenatal maternal low protein diet: ACE-2 in the mouse lung and sexually dimorphic programming of hypertension. 2597 47

Inferring drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their functions through interacting others. To overcome this issue, we propose a novel methodology that discover the drug-disease association based on protein complexes. Firstly, the integrated heterogeneous network consisting of drugs, protein complexes, and disease are constructed, where we assign weights to the drug-disease association by using probability. Then, from the tripartite network, we get the indirect weighted relationships between drugs and diseases. The larger the weight, the higher the reliability of the correlation. We apply our method to mental disorders and hypertension, and validate the result by using comparative toxicogenomics database. Our ranked results can be directly reinforced by existing biomedical literature, suggesting that our proposed method obtains higher specificity and sensitivity. The proposed method offers new insight into drug-disease discovery. Our method is publicly available at http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download.html.
BMC Med Genomics 2015
PMID:Inferring drug-disease associations based on known protein complexes. 2604 49

Diagnosing and treating hypertension plays an important role in minimising the risk of cardiovascular disease and stroke. Early and accurate diagnosis of hypertension, as well as regular monitoring, is essential to meet treatment targets. In this article, current recommendations for the screening and diagnosis of hypertension are reviewed. The evidence for treatment targets specified in contemporary guidelines is evaluated and recommendations from the USA, Canada, Europe and the UK are compared. Finally, consideration is given as to how diagnosis and management of hypertension might develop in the future.
BMC Med 2015 Oct 12
PMID:What is the evidence base for diagnosing hypertension and for subsequent blood pressure treatment targets in the prevention of cardiovascular disease? 2645 9

High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure.
BMC Genet 2016 Feb 03
PMID:Filtering genetic variants and placing informative priors based on putative biological function. 2686 82

Arterial hypertension and stroke are strong independent risk factors for the development of cognitive impairment and dementia. Persistently elevated blood pressure (BP) is known to impair cognitive function, however onset of new cognitive decline is common following a large and multiple mini strokes. Among various forms of dementia the most prevalent include Alzheimer's disease (AD) and vascular dementia (VaD) which often present with similar clinical symptoms and challenging diagnosis. While hypertension is the most important modifiable vascular risk factor with antihypertensive therapy reducing the risk of stroke and potentially slowing cognitive decline, optimal BP levels for maintaining an ideal age-related mental performance are yet to be established. Cognition has improved following the use of at least one representative agent of the major drug classes with further neuroprotection with renin angiotensin inhibitors and calcium channel blockers in the hypertensive elderly. However, a reduction in BP may worsen cerebral perfusion causing an increased risk of CV complications due to the J-curve phenomenon. Given the uncertainties and conflicting results from randomized trials regarding the hypertension management in the elderly, particularly octogenarians, antihypertensive approaches are primarily based on expert opinion. Herein, we summarize available data linking arterial hypertension to cognitive decline and antihypertensive approach with potential benefits in improving cognitive function in elderly hypertensive patients.
BMC Cardiovasc Disord 2016 11 03
PMID:Hypertension and cognitive dysfunction in elderly: blood pressure management for this global burden. 2780 79

Homozygosity disequilibrium (HD) describes a nonrandom pattern of sizable runs of homozygosity (ROH) that deviated from a random distribution of homozygotes and heterozygotes in the genome. In this study, we developed a double-weight local polynomial model for estimating homozygosity intensity. This new estimation method enables considering the local property and genetic information of homozygosity in the human genome when detecting regions of HD. By using this new method, we estimated whole-genome homozygosity intensities by analyzing real whole-genome sequencing data of 959 related individuals from 20 large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). Through the analysis, we derived the distribution of HD in the human genome and provided evidence for the genetic component of natural variation in HD. Generalized estimating equation analysis for 855 related individuals was performed to identify regions of HD associated with diastolic blood pressure (DBP), systolic blood pressure, and hypertension (HTN), with concomitant adjustment for age and sex. We identified one DBP-associated and 2 HTN-associated regions of HD. We also studied the gene regulation of HD by analyzing the real whole-genome transcription data of 647 individuals. A set of gene expressions regulated by the DBP- and HTN-associated regions of HD was identified. Finally, we conducted simulation studies to evaluate the performance of our homozygosity association test. The results showed that the association test had a high power and that type 1 error was controlled. The methods have been integrated into our developed Loss-of-Heterozygosity Analysis Suite software, which can be downloaded at http://www.stat.sinica.edu.tw/hsinchou/genetics/loh/LOHAS.htm.
BMC Proc 2016
PMID:Homozygosity disequilibrium and its gene regulation. 2798 Jun 29

Statistical association tests for rare variants can be classified as the burden approach and the sequence kernel association test (SKAT) approach. The burden and SKAT approaches, originally developed for case-control analysis, have also been extended to family-based tests. In the presence of both case-control and family data for a study, joint analysis for the combined data set can increase the statistical power. We extended the Combined Association in the Presence of Linkage (CAPL) test, using both case-control and family data for testing common variants, to rare variant association analysis. The burden and SKAT algorithms were applied to the CAPL test. We used simulations to verify that the CAPL tests incorporating the burden and SKAT algorithms have correct type I error rates. Power studies suggested that both tests have adequate power to identify rare variants associated with the disease. We applied the tests to the Genetic Analysis Workshop 19 data set using the combined family and case-control data for hypertension. The analysis identified several candidate genes for hypertension.
BMC Proc 2016
PMID:A combined association test for rare variants using family and case-control data. 2798 Jun 39


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